Distributed sensor networks are frequently used for complex sensing applications including, for example, the monitoring of local events via acoustic sound detection over a large measurement space. This includes the detection of acoustic emission sources such as fluid leaks, mechanical impact, sliding contact, fluid cavitation, wear and friction of large gas turbines and others. Often the events of interest occur at an unknown time and location and can only be observed accurately with nearby sensors. For selected applications, a sensor network may be moved to a nearby sound detection location such as when conducting product sound emission characterization in a controlled test environment.
However, movement of the sensor network is not desirable or feasible for applications wherein large sensor networks are used to monitor sound emissions of equipment in the field, such as in oil exploration, oil field monitoring, submarine detection and other applications. For such applications, it is desirable to monitor an entire sensor network and adaptively focus on areas of interest e.g., if an event of interest occurs at an unpredictable location. Such sensor networks require a relatively large sensor density which results in a relatively large number of sensors. For example, more than 1000 sensors may be used in order to provide sufficient sensor density. However, the sensors used in such networks are expensive and complex. Further, it is important that the exact location of each sensor is known. Thus, it is difficult to deploy such networks in a time and cost effective way since the physical dimensions of each installation vary.